Find research datasets worth reusing

opencc-by-4.0Sep 2024View details →

Calibration of non-local damage models from full-field measurements: application to discrete element fields.

The codes, datasets, and results from the manuscript  'Calibration of non-local damage models from full-field measurements: application to discrete element fields' are available here.   This repository is organized into four folders: <ol> <li>'Ideal Case' folder: This corresponds to Section ‘3. Application of the calibration method on a 1D ideal case' of the manuscript. In this folder, you will find the <a href="https://freefem.org/" target="_blank" rel="noopener">FreeFEM+</a> and <a href="https://www.python.org/" target="_blank" rel="noopener">Python</a> scripts and a makefile to run them. These codes reproduce all the figures, as well as the complete dataset associated with Figures 1-7.</li> <li>'Virtual Testing Machine' folder: This corresponds to Section 4, 'Introduction of a Virtual Testing Machine' of the manuscript. Here, you will find: <ul> <li>The data for Figure 9, contains the force vs. CMOD response for all sizes and geometries.</li> <li>The data for Figure 10, contains the scores associated with this parametric identification.</li> </ul> </li> <li>'Green Functions' folder: This includes the <a href="https://freefem.org/" target="_blank" rel="noopener">FreeFEM++</a> code to solve the boundary value problem for obtaining Green's function of the Eikonal equation. The code is demonstrated with both a non-damaged case and a polynomial damage case. In the first case, Green’s function corresponds to the weighting functions of the Implicit Gradient method (<a href="https://doi.org/10.1002/(SICI)1097-0207(19961015)39:19<3391::AID-NME7>3.0.CO;2-D" target="_blank" rel="noopener">Peerlings et al., 1996</a>).</li> <li>'Real Case' folder: This corresponds to Section 5, 'Application of the Proposed Calibration Procedure to Virtual Tests.' Here, you will find: <ul> <li>The complete 'Damage vs. Strain' Dataset was generated with the Virtual Testing Machine. Figures 12-15 correspond to this dataset, processed as explained in the manuscript.</li> <li>The 'damage vs. damage driving variable' dataset for each characteristic length.  Figure 16 in the manuscript corresponds to this dataset.</li> <li>The data for Figures 17 and 18, contain the evolution of the error with the characteristic length for different evolution laws.</li> </ul> </li> </ol>

opencc-by-4.0Nov 2021View details →

2021 5Genesis Berlin Platform Field Trial RTT and Throughput Measurements

RTT and Throughput measurements from the various measurement endpoints of the 2021 5GENESIS Berlin Platform Field Trials at IHP, in Frankfurt (Oder).

opencc-by-4.0Dec 2023View details →

High-frequency wind (u, w, v, Ts) and gas concentration measurements of CO2 and H2O over an agricultural field in Braunschweig, Germany

This dataset contains high-frequency eddy covariance (EC) measurements over a flat agricultural field at the Thünen Institute in Braunschweig, Germany (52.30° N, 10.45° E). The data collection period spanned 77 days in the year 2020 split into three files   <table> <tbody> <tr> <td>BS2020_06.rds</td> <td>June 11 to July 15</td> </tr> <tr> <td>BS2020_10.rds</td> <td>October 1 to November 10</td> </tr> <tr> <td>BS_2020_07_subset.rds</td> <td>July 11 to July 25</td> </tr> </tbody> </table>   Included in the dataset are 3D wind velocity data, recorded using a uSonic-3 Class A sonic anemometer from Metek GmbH. Additionally, the dataset provides gas concentration measurements for carbon dioxide (CO2) and water vapor (H2O), captured using an LI-7500A open-path infra-red gas analyzer from LI-COR Biosciences GmbH, Germany. Variables in the dataset <table> <tbody> <tr> <th>Variable Name</th> <th>Description</th> <th>Units</th> </tr> </tbody> <tbody> <tr> <td>time</td> <td>Unique time stamp (POSIXct format)</td> <td>Seconds since Unix epoch</td> </tr> <tr> <td>CO2</td> <td>Wet molar density of carbon dioxide</td> <td>µmol m⁻³</td> </tr> <tr> <td>H2O</td> <td>Wet molar density of water vapor</td> <td>mmol m⁻³</td> </tr> <tr> <td>Ts</td> <td>Sonic temperature</td> <td>Kelvin</td> </tr> <tr> <td>u, v, w</td> <td>3D wind velocity components</td> <td>m s⁻¹</td> </tr> </tbody> </table>     <div> <div> <div> The dataset is in RDS format (version 3), compatible with R version 3.5.0 or higher. RDS is a binary file format native to the R programming environment </div> </div> </div>

opencc-by-4.0Dec 2019View details →

3D displacement field and fault-offset measurements for the northern Kaikōura ruptures

Contents: 1. East, north and vertical components of the co-seismic displacement field for three faults (the Kekerengu, Jordan and Upper Kowhai faults) that ruptured in the 2016 Kaikoura earthquake, New Zealand (east.tif, north.tif, vertical.tif). 2. Shapefiles containing offsets across the faults of interest, measured from the displacement field (shapefiles.zip). 3. CSV files containing offsets across the faults of interest, measured from the displacement field (csvs.zip).  Our methodology is described in the following manuscript: Howell et al., 2019. 3D surface displacements during the 2016 MW 7.8 Kaikōura earthquake (New Zealand) from photogrammetry-derived point clouds, Journal of Geophysical Research Solid Earth, submitted.

opencc-by-4.0Aug 2024View details →

Magnetic Field Measurements above a Phonolite Diatreme near Rockeskyll, West Eifel, Germany

Open the record for dataset details and reuse information.

opencc-by-4.0Feb 2023View details →

OME-Zarr 3D hiPSCs with 3D labels & 3D measurements, 2x2 field of views

These are 2 small OME-Zarr files of the data from <a href="../records/7057076">10.5281/zenodo.7057076</a>. The images have been processed using <a href="https://fractal-analytics-platform.github.io/">Fractal</a>, the workflow is attached as a json file. It ran with fractal-server==2.3.6, fractal-client==2.0.1, fractal-web==1.4.0 and fractal-tasks-core==1.2.1. Both Zarr files are Zip-compressed to allow easier upload & download from Zenodo.  20200812-CardiomyocyteDifferentiation14-Cycle1.zarr contains 3 3D channels, a nuclear segmentation produced by <a href="https://cellpose.readthedocs.io/en/latest/">cellpose</a> as labels and 4 tables: A ROI table for the whole well, a ROI table for the 4 field of views, a masking ROI table for the nuclear segmentation, as well as measurements performed with <a href="https://github.com/haesleinhuepf/napari-skimage-regionprops">napari-skimage-regionprops</a>. 20200812-CardiomyocyteDifferentiation14-Cycle1_mip.zarr contains the same 3 channels, but as maximum intensity projections. It contains nuclear segmentation through cellpose, as well as 3 more labels generated by napari workflows (different thresholds, less accurate segmentations). It also contains 7 tables: The region of interests like in the 3D data, as well as measurements performed with <a href="https://github.com/haesleinhuepf/napari-skimage-regionprops">napari-skimage-regionprops</a>. The tables are stored in the OME-Zarr file according to the <a href="https://fractal-analytics-platform.github.io/fractal-tasks-core/tables/">Fractal table specification</a> spec in AnnData. The 3 channels are: - 0: DAPI, nuclear stain - 1: nanog, antibody staining with Bio-Techne AG, AF1997-SP, Lot KKJ0617121 for the stemness marker nanog - 2: Lamin B1, antibody staining with Abcam, ab16048, Lot GR3244890-2 for the nuclear envelope marker Lamin B1

opencc-by-4.0Aug 2024View details →

Data for: Multi-year field measurements of home storage systems and their use in capacity estimation

The dataset accompanies the Nature Energy publication by Figgener et al. (2024), Multi-year field measurements of home storage systems and their use in capacity estimation, <a href="https://doi.org/10.1038/s41560-024-01620-9">DOI 10.1038/s41560-024-01620-9</a>.  In addition, we use the dataset in Figgener et al. (2024), Degradation mode estimation using reconstructed open circuit voltage curves from multi-year home storage field data, <a href="https://doi.org/10.48550/arXiv.2411.08025">DOI 10.48550/arXiv.2411.08025</a> The ISEA / CARL of RWTH Aachen University measured 21 private home storage systems in Germany over up to eight years from 2015 to 2022. All these storage systems are combined with residential photovoltaic systems to increase self-consumption. The measured quantities published are system-level battery current, voltage, power, battery pack housing temperature, and room temperature. The sample rate is one second. The dataset consists of 106 system years, 14 billion data points, and 1,270 monthly files stored in 21 system folders.  Use the data as follows: 1. Download the data (Data_ID_01.zip to Data_ID_21.zip) and the belonging repository (Metadata_and_Code.zip) 2. Uncompress the files so that the uncompressed folders have the same name as the .zip files. 3. Copy all data folders in folder "Metadata_and_Code/00_Data/01_Operational_Data". Read and execute the file "StartUp_Read_and_Execute.m" and stay in this folder for any script you execute.  In addition, a detailed description of the dataset and how to use it can be found in the supplementary information of the publication.

opencc-by-4.0Jul 2021View details →

UAV outputs and associated field measurement of the herbaceous and tree of the Senegalese savanna of the Dahra Djoloff research center

The dataset contains UAV outputs (mosaic , surface model and terrain) and the associated measurements of vegetation( herbaceous and woody) that were made within the research isra station of Dahra Djoloff. Sites The sites were 38 ha-1 plots across the research station. The UAV were collected on the same site at the same date in October 2018(end of the wet season and maximum of the biomass). The sites were the sites of previous studies (Raynal 1964, Ndiaye et al. 2014, Ndiaye et al. 2015). The plots were chosen based on several studies of vegetation dynamics and these plots were judged to be representative of the diversity of vegetation type within the research station. UAV flight plan We used a low-cost UAV with an RGB (Red Green Blue) captor integrated in the UAV. The plots were mapped using a Dji Spark UAV with the litchi application for the automatic flight. The flight plan was six 100 m transects each separated by 20 m was performed at an altitude of 80 m and at a speed of 5 m.s-1. Images were acquired in autofocus mode (ISO exposure were automatically adjusted) at two-second intervals throughout the flight. The angle of view was 80°. The frontal overlap was about 90% and the side overlap about 80% with 80° angle Field measurement. Herbaceous Biomass. For the Landscape dataset, 10 squares of 1 m² were sampled; All the aboveground biomass was cut and weighted in fresh. A composite sample was made for each site and weighted dry to evaluated the dry matter content and so the dry matter of each sample. The positions of the squared was mark r with a plastic bag on the ground. Tree measurement. For the landscape, we selected 10 trees on the UAV maps. The measurements were made after image analysis in January 2019 and January 2020. The trees were not measured on all the site. The measured variables were the maximum height of the tree (using a clinometer), the diameter of the tree crown in the north-south direction and in the west-east direction. Their tree crown area was calculated assuming that the crown was a circle. The trunk diameters were measured at 0.30 cm in both direction and the circumference were calculated. All woody species were identified at the species and genus levels. Image analysis. The images taken during each flight were processed using a PiX4D mapper (Pix4D SA, Lausanne, Switzerland). 3D mapping is the basic parameter proposed in the software. For each plot, an orthophotograph, a digital surface model, and a digital elevation model were computed and exported in GeoTIFF format. Data organization For each plot, we had <ul> <li>DSM that contains the surface model in tiff</li> <li>DTM that contains the terrain model in tiff</li> <li>Mosaic that the orthomosaic in tiff.</li> </ul> All the different geotiff can directly be download. Data are in a zip file that contains the shapefile with the position and table with the field measurements. The shapefile “Herbaceous.shp” contain the positions of the squared sample but also of squared that contains only soil (squared cut before the flight). The CSV “Herbaceous-landscape.csv” contains the measurement of Aboveground biomass. (FM fresh mass and DM dry mass). Both are in g (g.m-²). The biomass was available for 346 squared. The shapefile “tree.shp” contains the positions of the tree. Here the shapefile contains the positions of all the tree preselected on the map. Only a selection of theses tree was measured on the field. The file “Tree-landscape.csv” contains the tree measurements with the species, the height (in m), the trunk circumference (TC) in cm and the area of the crown(area) in m². The tree measurements were available for 240 trees.   reference Ndiaye, O., A. T. Diop, L. E. Akpo, and M. Diène. 2014. Dynamique de la teneur en carbone et en azote des sols dans les systèmes d’exploitation du Ferlo: cas du CRZ de Dahra. Journal of Applied Biosciences 83:7554-7569. Ndiaye, O., A. T. Diop, M. Diène, and L. E. Akpo. 2015. Étude comparée de la végétation de 1964 et 2011 en milieu pâturé: Cas du CRZ de Dahra. Journal of Applied Biosciences 88:8235–8248. Raynal, J. 1964. Etude botanique de pâturages du Centre de Recherches Zootechniques de Dahra-Djoloff (Sénégal).

opencc-by-4.0Jul 2021View details →

UAV outputs and associated field measurement of the herbaceous and tree of the Senegalese savanna across Senegal

This dataset contains UAV outputs (mosaic, surface and terrain model) and field measurement of vegetation that were made in northern and Eastern Senegal. Sites National gradient measurements For the national gradients, the measurements were made on 45 different plots in two different field campaign. One in the Northern part at the end of September 2020 and the other in South eastern part of Senegal in middle of October. The selection of the site was a combination of accessibility (not far from the road) and diversity of vegetation. The average rainfall for the period 1981-2018 was ranging from 221 mm.y-1 to 468 mm. y-1 for the Northern Part and ranging 759 mm.y-1 to 1246 mm y-1 for the south eastern part. UAV flight plan We used a low-cost UAV with an RGB (Red Green Blue) captor integrated in the UAV.  The UAV was an Anafi of Parrot with PIX4D capture application using the double gird flight plan in a square generally of 100m*100m; The height of the flight was 80m with an overlap of 80% at low speed with 80° angle °.  The flights were made at any time during the day. Field measurement. Herbaceous Biomass. 3 squares of 1 m² were sampled. All the aboveground biomass was cut and weighted in fresh. A composite sample was made for each site and weighted dry to evaluated the dry matter content and so the dry matter of each sample. The height of 5 herbaceous individuals selected randomly were measured. We recorded the species composition with percentage of cover of each species. We collected an herbarium sample each time we had a new species. The sample were used to identified the species by the IFAN herbarium team. The positions of the squared was mark with a wood triangle painted on the ground. Tree measurement. Four trees were measured on the field. It was the four woody individuals the closest to the first square of herbaceous measurements were made in each direction (Northwest, North east, South West, South East). The distance to the first square of each tree were measured using a telemeter. The height was also measured with a laser telemeter. The circumference at 0.30cm and 1.3 cm were measured. The diameter of the tree crown in the north-south direction and in the west-east direction were measured to the crow area calculated assuming that the crown was a circle. The species were recorded. We collected an herbarium sample each time we had a new species. The sample were used to identified the species by the IFAN herbarium team. Image analysis. The images taken during each flight were processed using a PiX4D mapper (Pix4D SA, Lausanne, Switzerland). 3D mapping is the basic parameter proposed in the software. For each plot, an orthophotograph, a digital surface model, and a digital elevation model were computed and exported in GeoTIFF format. Data organization The data are organized in two separated folders for each dataset. Each dataset folders contains four folders: <ul> <li>DSM that contains the surface model in tiff</li> <li>DTM that contains the terrain model in tiff</li> <li>Mosaic that the orthomosaic in tiff.</li> <li>Data that contains the shapefile with the position and table with the field measurements.</li> </ul> The shapefile” national-shape.shp" contains the positions of both tree and herbaceous samples. In some case it was hard to position the squared or the tree. The position and the shape of the object are not well defined. The file “tree-national.xlsx” contains the information on the tree measurement. The ID that contains the site and the positions of the trees, the distance from the squared in m that indicate the distance of the tree to the biomass square. The height H (in m), the trunk circumference at 1.30m (TC1.3) and at 0.3m(TC0.3) in cmand the area of crown (Area). The species is also described. The file “ herbacous_national.xlsx” contains the information on the herbaceous layer. For each square, the height of the herbaceous layer (H), Fresh mass (FM), Dry matter content (DMC) and dry Mass (DM) are presented; The last columns of the file are the different species with the percentage of cover in each case.

opencc-by-4.0Feb 2023View details →

Electron microscopy of particles collected by different techniques from field measurements in the Moroccan Sahara during FRAGMENT 2019

An intensive field campaign between 4-30 September 2019 was conducted at a major source region on the edge of the Saharan desert in Morocco (29.83 °N 5.87 °W) in the context of the FRontiers in dust minerAloGical coMposition and its Effects upoN climaTe (FRAGMENT) project. Samples were collected with three different sampling techniques, namely: flat-plate sampler (FPS), free-wing impactor (FWI), and a micro-orifice uniform deposit impactor (MOUDI). Substrates in the MOUDI and FWI were collected two times a day with a typical sampling duration of a few minutes to avoid overloading the substrate for individual particle analysis. For the flat-plate sampler, the average exposure time was half a day. Here we present dataset of the elemental composition and morphology of more than 300,000 freshly emitted individual particles by performing offline analysis in the laboratory using Scanning Electron Microscopy (SEM) coupled with Energy-Dispersive X-ray Spectrometry (EDX).

opencc-by-4.0May 2023View details →

Data presented in González-Flórez et al. 2023 "Insights into the size-resolved dust emission from field measurements in the Moroccan Sahara", Atmos. Chem. Phys.

Meteorological, dust and saltation data used in González-Flórez et al., 2023. Data are based on measurements taken during an intensive dust field campaign conducted in the context of the FRontiers in dust minerAloGical coMposition and its Effects upoN climaTe (FRAGMENT) project. The campaign took place in September 2019 in a small ephemeral lake, locally named "L'Bour", located in the Lower Drâa Valley in Morocco. The description of the data is provided below: - t.nc: time series of temperature measured with four aspirated shield temperature sensors (Campbell Scientific 43502 fan-aspirated shield with 43347 RTD Temperature probe) placed at heights of 1m, 2m, 4m and 8m. - t005.nc time series of temperature measured with a temperature and relative humidity probe (Campbell Scientific HC2A-S3) at 0.5m height. - rh005.nc: time series relative humidity measured with a temperature and relative humidity probe (Campbell Scientific HC2A-S3) at 0.5m height. - wspd.nc: time series of wind speed measured with five 2-D sonic anemometers  (Campbell Scientific WINDSONIC4-L) placed at heights of 0.4m, 0.8m, 2m, 5m and 10m. - sdir.nc: time series of wind direction measured with five 2-D sonic anemometers  (Campbell Scientific WINDSONIC4-L) placed at heights of 0.4m, 0.8m, 2m, 5m and 10m. - radout.nc: time series of outgoing long wave radiation measured with a four-component net radiometer  (Campbell Scientific NR01-L radiometer) placed at 1.5m height. - p015.nc: times series barometric pressure measured with a barometer (Campbell Scientific CS106) at around 1.5m height. - u_star_law.nc: time series friction velocity calculated through the law of the wall method. - z0_law.nc: time series of roughness length calculated through the law of the wall method. - zeta_law.nc: time series of dimensionless height, zref/L, where zref is the reference height (zref=2m) and L is the Obukhov length  calculated through the law of the wall method. - psd_lower_15avg_20190904_000000_integrated_bins.nc: time series of 15-min average number concentrations in integrated size bin resolution measured with an optical particle counter (Fidas 200S, Palas GmbH) at ~1.8m height. - psd_upper_15avg_20190904_000000_integrated_bins.nc: time series of 15-min average number concentrations in integrated size bin resolution measured with an optical particle counter (Fidas 200S, Palas GmbH) at ~3.5m height and corrected for systematic bias based on an intercomparison between the two Fidas at the end of the campaign. - diff_flux_nb_15avg_20190904_000000_integrated_bins.nc: time series of 15-min average number diffusive flux calculated using the flux-gradient method. - q_15avg.nc: time series of 15-min average saltation flux calculated based on measurements with optical gate devices at heights of 0.05m, 0.15m and 0.3m as part of the Standalone AeoliaN Transport Real-time Instrument (SANTRI, Desert Research Institute). - geometric_diameters_integrated_size_bins.csv: containing the minimum, maximum and mean logarithmic optical diameter of the integrated size bins. - optical_diameters_integrated_size_bins: containing the minimum, maximum and mean logarithmic geometric diameter of the integrated size bins. SANTRI data were processed by Martina Klose (<a href="mailto:martina.klose@kit.edu">martina.klose@kit.edu</a>) and the rest by Cristina González Flórez (<a href="mailto:cristina.gonzalez@bsc.es">cristina.gonzalez@bsc.es</a>). Please, cite González-Flórez et al. (2023, ACP) if you use these data. If the data become the key main component of a paper then co-authorship may be offered. Contact Carlos Pérez García-Pando (<a href="mailto:carlos.perez@bsc.es">carlos.perez@bsc.es</a>) if more details are needed. This work has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 773051, FRAGMENT).

opencc-by-4.0Jun 2023View details →

'Danum Revised' forest field plot data for thirty-five 0.28 hectare field plots, measured in 2020

Permanent forest plot data from Sabah, Malaysia. Danum Valley Conservation Area. Here we provide a brief description of the permanent plot data collected following the same protocols utilized within the Kuamut forest reserve.Twenty circular field plots with a 30 m radius that were established across the protected area as part of a collaboration between SEARRP and the Carnegie Airborne Observatory (CAO) in 2017. See <a href="https://doi.org/10.5281/zenodo.8042050">'Danum Revised' forest field plot data for twenty 0.28 hectare field plots, measured in 2017</a>In 2020 we added an additional 15 plots as part of work for Keller et al. 'Biodiversity consequences of long-term active forest restoration in selectively-logged tropical rainforests', and so this dataset is the full 35 field plots measured in 2020.These plots were surveyed following the same protocols as those described by the Kuamut conservation project. The field protocol is provided. The original 20 Field plot locations where selected using airborne LiDAR to specifically target both high Aboveground Carbon Density (ACD) areas, and also areas with different ACD predictions based on a draft set of carbon mapping  models. See Asner et al. (2017) and Jucker et al. (2018) for further details.In 2020 we selected existing these additional 15 plot points using a high resolution biomass map (see: Asner et al. 2017; Asner et al. 2021). In order to represent the entirety of the area sufficiently, we chose plot locations to represent the average biomass value and variation around it of the majority of the managed area (<a href="https://doi.org/10.1126/science.aay4490">cf. Philipson et al. (2020) for regional values</a>). The plot location further must have fulfilled the following criteria: (1) plots had to be at least 100 m away from noticeable features such as roads to avoid edge effects (but for logistic reasons they nevertheless had to be in the vicinity of roads); (2) plots had to be spread out as much as logistically feasible; (see Keller et al. (submitted), for full details). Note Keller et al. (submitted), used 30 plots for a balanced design between naturally regenerating and actively restored areas. Here we present all 35 plots measured in 2020. Plot locations where additionally updated using an SXblue GPS differential with SBAS augmentation. These locations are provided.Use of these data require citation of this dataset, the associated publication (Keller et al. submitted), as well as the <a href="https://doi.org/10.5281/zenodo.8042050">2017 dataset</a> (which describes the full set up process),.  We also require that you inform us of the use of the data would also appreciate the opportunity to be involved in work making use of this data. The required citations are follows:Philipson, Christopher D., Brodrick, Philip G., Milne, Sol, Murus, Remmy, Rasion, Joulu, Reynolds, Glen, Siew Yee, Hii, Ulok, Philip Ak, & Asner, Gregory P. (2023). 'Danum Revised' forest field plot data for twenty 0.28 hectare field plots, measured in 2017 (1.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.8042050">https://doi.org/10.5281/zenodo.8042050</a>Keller et al. (submitted). 'Biodiversity consequences of long-term active forest restoration in selectively-logged tropical rainforests',This work would not be possible without the incredibly hard work and dedication of the entire SEARRP field team, including Philip Ak Ulok; Hii Siew Yee; Remmy Bin Murus; Alexander Karolus; Andy Brian Karolus; Frederica Karolus; Zidey Fulgentius; Welday Bin Girang; Mohamad Taufiq Bin Sumin, Mohd Fadil Bin Abd Karim, Joulu Rasion, and Japin Bin Rasion.  We are very grateful to the help of all the field staff not specifically mentioned here. ReferencesAsner, Gregory P., Philip G. Brodrick, Christopher Philipson, Nicolas R. Vaughn, Roberta E. Martin, David E. Knapp, Joseph Heckler, et al. 2017. "Mapped Aboveground Carbon Stocks to Advance Forest Conservation and Recovery in Malaysian Borneo." Biological Conservation 217 (June 2017): 289–310. <a href="https://doi.org/10.1016/j.biocon.2017.10.020">https://doi.org/10.1016/j.biocon.2017.10.020</a>Jucker, Tommaso, Gregory P. Asner, Michele Dalponte, Philip G. Brodrick, Christopher D. Philipson, Nicholas R. Vaughn, Yit Arn Teh, et al. 2018. "Estimating Aboveground Carbon Density and Its Uncertainty in Borneo's Structurally Complex Tropical Forests Using Airborne Laser Scanning." Biogeosciences 15 (12): 3811–30. <a href="https://doi.org/10.5194/bg-15-3811-2018">https://doi.org/10.5194/bg-15-3811-2018</a>Keller, Nadine, Pascal A Niklaus, Jaboury Ghazoul, Tobias Marfil, Elia Godoong, and Christopher D Philipson. 2023. "Biodiversity Consequences of Long-Term Active Forest Restoration in Selectively-Logged Tropical Rainforests." Forest Ecology and Management 549: 4. <a href="https://linkprotect.cudasvc.com/url?a=https%3a%2f%2fdoi.org%2f10.1016%2fj.foreco.2023.121414&c=E,1,CfW3xjatfMP0AonRryjLaKUmg50KQQtF2-n0UPlyW_NATChUbrP9yczm8IQKqWaiNSMtm0g3HuVHyW8N7CKr84VGIQokuIogx-7OG-AAY5epGc0i&typo=1">https://doi.org/10.1016/j.foreco.2023.121414</a>.Philipson, Christopher D., Mark E.J. Cutler, Philip G. Brodric, Gregory P. Asne, Doreen S. Boy, Pedro Moura Costa, Joel Fiddes, et al. 2020. "Active Restoration Accelerates the Carbon Recovery of Human-Modified Tropical Forests." Science 369 (6505): 838–41. <a href="https://doi.org/10.1126/science.aay4490">https://doi.org/10.1126/science.aay4490</a>Philipson, Christopher D., Brodrick, Philip G., Milne, Sol, Murus, Remmy, Rasion, Joulu, Reynolds, Glen, Siew Yee, Hii, Ulok, Philip Ak, & Asner, Gregory P. (2023). 'Danum Revised' forest field plot data for twenty 0.28 hectare field plots, measured in 2017 (1.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.8042050">https://doi.org/10.5281/zenodo.8042050</a>

openCC (other)Apr 2022View details →

University of Kansas Field Station: Forest demography, 1980 – 2015. On ten study plots established on three management units all live trees with a dbh > 7.5 cm (3 in) were identified to species, measured, and tagged. Trees were initially measured in 1980/1981 and re-measured in three successive time periods: 1993/95; 2002/03; and 2014/15. Trees will be measured again in 2025/26.

In 1980 researchers at the University of Kansas initiated a long-term experiment monitoring the composition of oak-hickory forest communities at the University’s field station near Lawrence, Kansas. The purpose of the study was to determine how forest species composition varied temporally across distinct habitats that varied in topography, elevation, sun exposure, management history and successional stage. Ten permanent sites were sampled approximately each decade with data collection periods of 1980/81, 1993/95, 2002/03, and 2014/15. Trees with a minimum diameter at breast height (dbh) of ≥ 7.5 cm were tagged, identified to species and measured. Trees will be measured again in 2025/26.

openCC (other)Nov 2017View details →

Meteorological Field Measurements in Yahara River Watershed

These data are collected to support the Water Sustainability and Climate of the Yahara River Watershed Project. Meteorological measurements include air temperature, relative humidity, wind speed, gust wind speed, solar radiation, dew point temperature, and rainfall. Weather stations were installed at 3 locations across the Yahara Watershed at the UW Arboretum, the City of Madisons Cherokee Park, and Waunakee Marsh State Wildlife Area. These data are being collected to observe differences in how water and energy are processed under different land cover types and to create calibration datasets for a separate project goal of creating an agro-biophysical model of the entire Yahara River watershed.

Sevilleta Field Station Meteorological Network (SevMET): High frequency measurements from the Black Butte Meteorological Station (BLBT), Sevilleta National Wildlife Refuge, NM, USA, 2024-ongoing.

The Sevilleta Field Station Meteorological Network (SevMET) is a spatially distributed, long-term climate monitoring network established to enhance and expand climate monitoring across a variety of dryland ecosystems (e.g., grasslands, shrublands, woodlands) within the Sevilleta National Wildlife Refuge in central New Mexico. Ecosystem processes in drylands are strongly regulated by climatic drivers that are highly variable in space and time, both within and among years. Therefore, accurate measurement of environmental variables at high spatial and temporal resolution is fundamental to understanding biophysical processes in these ecosystems. SevMET consists of fifteen standardized research-grade weather stations located across multiple dryland ecosystem types (e.g., grasslands, shrublands, woodlands) representative of the southwestern US. Stations continuously measure a standard suite of meteorological variables at five-minute intervals, including air temperature, relative humidity, precipitation, photosynthetically active radiation, incoming shortwave radiation, wind speed and direction, dew point, vapor pressure, and, at a subset of stations, barometric pressure. Stations also measure a suite of soil parameters (bulk electrical conductivity, dielectric permittivity, temperature, and volumetric water content) at six depths (5, 10, 20, 30, 40, and 50 cm) below the ground surface using 1-2 integrated soil profilers. Additionally, phenocams at each station capture images at thirty-minute intervals during daylight hours. This data package contains high-frequency meteorological measurements from the Black Butte Meteorological Station (BLBT). Phenocam images can be accessed through the PhenoCam Network at: https://phenocam.nau.edu/webcam/sites/sevmetblbt/. These data complement and extend meteorological data recorded by an adjacent station (Met54), accessible at: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-sev&identifier=1.

Sevilleta Field Station Meteorological Network (SevMET): High frequency measurements from the Bronco Well Meteorological Station (BRWL), Sevilleta National Wildlife Refuge, NM, USA, 2024-ongoing.

The Sevilleta Field Station Meteorological Network (SevMET) is a spatially distributed, long-term climate monitoring network established to enhance and expand climate monitoring across a variety of dryland ecosystems (e.g., grasslands, shrublands, woodlands) within the Sevilleta National Wildlife Refuge in central New Mexico. Ecosystem processes in drylands are strongly regulated by climatic drivers that are highly variable in space and time, both within and among years. Therefore, accurate measurement of environmental variables at high spatial and temporal resolution is fundamental to understanding biophysical processes in these ecosystems. SevMET consists of fifteen standardized research-grade weather stations located across multiple dryland ecosystem types (e.g., grasslands, shrublands, woodlands) representative of the southwestern US. Stations continuously measure a standard suite of meteorological variables at five-minute intervals, including air temperature, relative humidity, precipitation, photosynthetically active radiation, incoming shortwave radiation, wind speed and direction, dew point, vapor pressure, and, at a subset of stations, barometric pressure. Stations also measure a suite of soil parameters (bulk electrical conductivity, dielectric permittivity, temperature, and volumetric water content) at six depths (5, 10, 20, 30, 40, and 50 cm) below the ground surface using 1-2 integrated soil profilers. Additionally, phenocams at each station capture images at thirty-minute intervals during daylight hours. This data package contains high-frequency meteorological measurements from the Bronco Well Meteorological Station (BRWL). Phenocam images can be accessed through the PhenoCam Network at: https://phenocam.nau.edu/webcam/sites/sevmetbrwl/. These data complement and extend meteorological data recorded by an adjacent station (Met45), accessible at: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-sev&identifier=1.

Sevilleta Field Station Meteorological Network (SevMET): High frequency measurements from the Burris Well Meteorological Station (BUWL), Sevilleta National Wildlife Refuge, NM, USA, 2024-ongoing.

The Sevilleta Field Station Meteorological Network (SevMET) is a spatially distributed, long-term climate monitoring network established to enhance and expand climate monitoring across a variety of dryland ecosystems (e.g., grasslands, shrublands, woodlands) within the Sevilleta National Wildlife Refuge in central New Mexico. Ecosystem processes in drylands are strongly regulated by climatic drivers that are highly variable in space and time, both within and among years. Therefore, accurate measurement of environmental variables at high spatial and temporal resolution is fundamental to understanding biophysical processes in these ecosystems. SevMET consists of fifteen standardized research-grade weather stations located across multiple dryland ecosystem types (e.g., grasslands, shrublands, woodlands) representative of the southwestern US. Stations continuously measure a standard suite of meteorological variables at five-minute intervals, including air temperature, relative humidity, precipitation, photosynthetically active radiation, incoming shortwave radiation, wind speed and direction, dew point, vapor pressure, and, at a subset of stations, barometric pressure. Stations also measure a suite of soil parameters (bulk electrical conductivity, dielectric permittivity, temperature, and volumetric water content) at six depths (5, 10, 20, 30, 40, and 50 cm) below the ground surface using 1-2 integrated soil profilers. Additionally, phenocams at each station capture images at thirty-minute intervals during daylight hours. This data package contains high-frequency meteorological measurements from the Burris Well Meteorological Station (BUWL). Phenocam images can be accessed through the PhenoCam Network at: https://phenocam.nau.edu/webcam/sites/sevmetbuwl/. These data complement and extend meteorological data recorded by an adjacent station (Met50), accessible at: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-sev&identifier=1.

Sevilleta Field Station Meteorological Network (SevMET): High frequency measurements from the Contreras Meteorological Station (CONT), Sevilleta National Wildlife Refuge, NM, USA, 2024-ongoing.

The Sevilleta Field Station Meteorological Network (SevMET) is a spatially distributed, long-term climate monitoring network established to enhance and expand climate monitoring across a variety of dryland ecosystems (e.g., grasslands, shrublands, woodlands) within the Sevilleta National Wildlife Refuge in central New Mexico. Ecosystem processes in drylands are strongly regulated by climatic drivers that are highly variable in space and time, both within and among years. Therefore, accurate measurement of environmental variables at high spatial and temporal resolution is fundamental to understanding biophysical processes in these ecosystems. SevMET consists of fifteen standardized research-grade weather stations located across multiple dryland ecosystem types (e.g., grasslands, shrublands, woodlands) representative of the southwestern US. Stations continuously measure a standard suite of meteorological variables at five-minute intervals, including air temperature, relative humidity, precipitation, photosynthetically active radiation, incoming shortwave radiation, wind speed and direction, dew point, vapor pressure, and, at a subset of stations, barometric pressure. Stations also measure a suite of soil parameters (bulk electrical conductivity, dielectric permittivity, temperature, and volumetric water content) at six depths (5, 10, 20, 30, 40, and 50 cm) below the ground surface using 1-2 integrated soil profilers. Additionally, phenocams at each station capture images at thirty-minute intervals during daylight hours. This data package contains high-frequency meteorological measurements from the Burris Well Meteorological Station (BUWL). Phenocam images can be accessed through the PhenoCam Network at: https://phenocam.nau.edu/webcam/sites/sevmetcont/.

Sevilleta Field Station Meteorological Network (SevMET): High frequency measurements from the Cerro Montoso Meteorological Station (CRMT), Sevilleta National Wildlife Refuge, NM, USA, 2024-ongoing.

The Sevilleta Field Station Meteorological Network (SevMET) is a spatially distributed, long-term climate monitoring network established to enhance and expand climate monitoring across a variety of dryland ecosystems (e.g., grasslands, shrublands, woodlands) within the Sevilleta National Wildlife Refuge in central New Mexico. Ecosystem processes in drylands are strongly regulated by climatic drivers that are highly variable in space and time, both within and among years. Therefore, accurate measurement of environmental variables at high spatial and temporal resolution is fundamental to understanding biophysical processes in these ecosystems. SevMET consists of fifteen standardized research-grade weather stations located across multiple dryland ecosystem types (e.g., grasslands, shrublands, woodlands) representative of the southwestern US. Stations continuously measure a standard suite of meteorological variables at five-minute intervals, including air temperature, relative humidity, precipitation, photosynthetically active radiation, incoming shortwave radiation, wind speed and direction, dew point, vapor pressure, and, at a subset of stations, barometric pressure. Stations also measure a suite of soil parameters (bulk electrical conductivity, dielectric permittivity, temperature, and volumetric water content) at six depths (5, 10, 20, 30, 40, and 50 cm) below the ground surface using 1-2 integrated soil profilers. Additionally, phenocams at each station capture images at thirty-minute intervals during daylight hours. This data package contains high-frequency meteorological measurements from the Cerro Montoso Meteorological Station (CRMT). Phenocam images can be accessed through the PhenoCam Network at: https://phenocam.nau.edu/webcam/sites/sevmetcrmt/. These data complement and extend meteorological data recorded by an adjacent station (Met42), accessible at: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-sev&identifier=1.

Sevilleta Field Station Meteorological Network (SevMET): High frequency measurements from the Deep Well Meteorological Station (DPWL), Sevilleta National Wildlife Refuge, NM, USA, 2024-ongoing.

The Sevilleta Field Station Meteorological Network (SevMET) is a spatially distributed, long-term climate monitoring network established to enhance and expand climate monitoring across a variety of dryland ecosystems (e.g., grasslands, shrublands, woodlands) within the Sevilleta National Wildlife Refuge in central New Mexico. Ecosystem processes in drylands are strongly regulated by climatic drivers that are highly variable in space and time, both within and among years. Therefore, accurate measurement of environmental variables at high spatial and temporal resolution is fundamental to understanding biophysical processes in these ecosystems. SevMET consists of fifteen standardized research-grade weather stations located across multiple dryland ecosystem types (e.g., grasslands, shrublands, woodlands) representative of the southwestern US. Stations continuously measure a standard suite of meteorological variables at five-minute intervals, including air temperature, relative humidity, precipitation, photosynthetically active radiation, incoming shortwave radiation, wind speed and direction, dew point, vapor pressure, and, at a subset of stations, barometric pressure. Stations also measure a suite of soil parameters (bulk electrical conductivity, dielectric permittivity, temperature, and volumetric water content) at six depths (5, 10, 20, 30, 40, and 50 cm) below the ground surface using 1-2 integrated soil profilers. Additionally, phenocams at each station capture images at thirty-minute intervals during daylight hours. This data package contains high-frequency meteorological measurements from the Deep Well Meteorological Station (DPWL). Phenocam images can be accessed through the PhenoCam Network at: https://phenocam.nau.edu/webcam/sites/sevmetdpwl/. These data complement and extend meteorological data recorded by an adjacent station (Met40), accessible at: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-sev&identifier=1.